Exploratory - C. difficile by time
Nicholas Lesniak
2/9/2017
None
Clindamycin
Vancomycin
Ciprofloxacin
Ampicillin
Cefaperazone
Metronidazole
Streptomycin
diversity vs colonization
alpha diversity
communities colonized to higher levels have lower diversity (alpha)
association between cfu and alpha (invsimpson and shannon - NS)
cfu vs # of otus (NS)
beta diversity
highly infected communities are most different than untreated
separation between untreated mice and all the highly infected communities (>1e6)
communities that recover/elimnate cdifficile are more diverse
difference in diversity between highly infected
more change w/low diversity?
more change with high cfu?
need to remove dependence of daily sampling?
Alpha Diversity
## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.162 -2.412 -1.213 1.429 13.075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.241e+00 1.714e-01 30.58 < 2e-16 ***
## CFU -1.455e-08 2.479e-09 -5.87 8.24e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.272 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.06859, Adjusted R-squared: 0.0666
## F-statistic: 34.46 on 1 and 468 DF, p-value: 8.244e-09## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.753 -15.274 -4.381 12.851 72.405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.609e+01 1.047e+00 44.03 < 2e-16 ***
## CFU -1.104e-07 1.514e-08 -7.29 1.33e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.99 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.102, Adjusted R-squared: 0.1001
## F-statistic: 53.14 on 1 and 468 DF, p-value: 1.331e-12## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.65813 -0.51173 -0.06297 0.45801 2.09371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.899e+00 3.404e-02 55.801 < 2e-16 ***
## CFU -3.098e-09 4.923e-10 -6.293 7.15e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6498 on 468 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.07803, Adjusted R-squared: 0.07606
## F-statistic: 39.61 on 1 and 468 DF, p-value: 7.149e-10## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9720 -1.5949 -0.8509 0.8313 14.2621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.051e+00 1.503e-01 26.944 < 2e-16 ***
## CFU -6.395e-09 1.924e-09 -3.324 0.000978 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.435 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.0293, Adjusted R-squared: 0.02665
## F-statistic: 11.05 on 1 and 366 DF, p-value: 0.0009777## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.215 -12.009 -2.285 9.603 58.318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.882e+01 9.625e-01 40.329 < 2e-16 ***
## CFU -6.051e-08 1.232e-08 -4.913 1.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.59 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.06187, Adjusted R-squared: 0.0593
## F-statistic: 24.14 on 1 and 366 DF, p-value: 1.356e-06## [[1]]
## [[1]]
##
## Call:
## lm(formula = get(variable_name) ~ CFU, data = data_frame)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.43246 -0.41705 -0.08106 0.43371 1.47089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.674e+00 3.495e-02 47.885 < 2e-16 ***
## CFU -1.551e-09 4.473e-10 -3.468 0.000588 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5662 on 366 degrees of freedom
## (36 observations deleted due to missingness)
## Multiple R-squared: 0.03181, Adjusted R-squared: 0.02916
## F-statistic: 12.02 on 1 and 366 DF, p-value: 0.0005875
After talking with Pat (1/17/18) Does end point look like initial? are most similar the recovered ones? Split analysis by abx/dose/days recovered Resistance more similar than colonized? Start with high dose and 1 day recovery then look at how modulating the dose/recovery affects train model with low recovery and test with high recovery Show different context of day 0 compare differences in metro recovery how do susceptibility break points compare?
the metadata file has the following columns group CFU - ranges 0 to 8.1e8 with 601 NAs (most of NAs are on days when cdiff was not present, so can change to 0 expect for NAs after day 1) cage mouse day - ranges -11 to 10 abx - amp cef cipro clinda metro none strep vanc 405 379 83 190 339 3 362 312 dose - 0.1 0.3 0.5 0.625 1 10mg/kg 5 NA’s 304 253 653 112 339 273 136 3 dose abx cages mice
10 cipro
10 clinda
0.1 cef
0.3 cef 0.5 cef 1 metro 0.1 strep 0.5 strep 5 strep 0.1 vanc 0.3 vanc 0.625 vanc cdiff - if sample was treated challenged with C. difficile logical T(1770), F(303) delayed - if sample was allowed extra days to recover from abx treatment logical T(455), F(1618) preAbx - if sample collected prior to abx treatment logical T(154), F(1919) recovDays - how many days after stopping abx (metro and amp for 5 day recovery) range 1 to 5
only one mouse not given abx but is listed as preAbx F for -5 possible to denote mock abx treatment(?) question about mouse 600-2D-6 (cef - delivered via water) should be pre-antibiotic but is listed as F all other mice in cage are preAbx on day -6, except this one since this abx was delivered via drinking water, it is likely clerical error, need to write a check script to make sure all mice in each cage all are recorded to have the same treatment
## # A tibble: 100 x 6
## otu median_abundance rho pvalue pvalue_BH pvalue_bon
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Otu000003 1.26 -0.614 4.17e-50 7.76e-48 7.76e-48
## 2 Otu000064 0. -0.606 1.52e-48 1.41e-46 2.83e-46
## 3 Otu000070 0. -0.531 1.40e-35 6.88e-34 2.61e-33
## 4 Otu000006 0. -0.531 1.48e-35 6.88e-34 2.75e-33
## 5 Otu000041 0. -0.484 5.64e-29 2.10e-27 1.05e-26
## 6 Otu000010 1.65 0.482 1.05e-28 3.26e-27 1.96e-26
## 7 Otu000017 0.100 -0.470 3.47e-27 8.35e-26 6.46e-25
## 8 Otu000057 0. -0.470 3.59e-27 8.35e-26 6.68e-25
## 9 Otu000031 0. -0.459 6.39e-26 1.32e-24 1.19e-23
## 10 Otu000050 0. -0.453 3.83e-25 7.11e-24 7.11e-23
## # ... with 90 more rows
